|Acknowledgements||Table of Contents||Sections 1-3||Section 4||Sections 5-6|
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Densham, P.J. and M.P. Armstrong (1995) Human-computer interaction considerations for visual-interactive locational analysis. In Nyerges, T., D. Mark, R. Laurini, and M. Egenhofer (eds.) Cognitive Aspects of Human-Computer Interaction for Geographic Information Systems. Kluwer, Dordrecht: 179-196.
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During the course of the specialist meeting, participants developed a research
agenda for CSDM which centers around 2 major themes: tool development and tool
use. Research questions that relate to tool development can be grouped into
those concerned with assessing and defining the tool requirements of
individuals and groups, those that seek to exploit developments in cognate
fields, and those that focus on the peculiarly spatial aspects of CSDM. In the
case of tool use, research questions can be grouped into those that examine
representation, those that seek to assess the effectiveness of CSDM software,
and those that are concerned with the roles of users and mediators during CSDM
and how they relate to different forms of CSDM software.
One of the outcomes of the specialist meeting is that a cadre of researchers have discussed the impediments to the widespread adoption of CSDM and have developed a common understanding of the magnitude and relative importance of these impediments. This shared understanding provides a starting point for research under the aegis of the Initiative. Many of the participants were working on parts of this agenda before the specialist meeting, others have indicated that they will adopt elements of it in their own research. A WWW server is planned to help these researchers coordinate their work and to be informed of what others are doing.
It is important to note that the formal termination of the initiative
(currently planned for the summer of 1997) will not signal the end of research
on CSDM. Rather, the research carried out during the life-span of the
initiative will further refine the research agenda and make it accessible to a
wider research community.
Related research activities at University of California at Santa Barbara
At UCSB, a small working group has been formed to continue work on topics
related to this research initiative. This working group has defined six major
research areas in Collaborative Spatial Decision Making resulting from
discussions at this meeting. Most of these research areas are not unique to
the spatial domain, but their solutions in the spatial domain require
modification of existing models and development of new models and model
Assess the usefulness of existing representations of spatial information for
representing the spatial aspects of the interests of participants in multiparty
Using a spatial decision support system to model and analyze spatial problems
requires an adequate representation of the objectives and interests of the
participants of the problem. This requires a sophisticated understanding of
the geographical conceptions of the problem that are inherent in participants'
interests. While one representation may be appropriate for one group and their
interests, it may not adequately represent others. If the representations of
the interests that are used in various models or presentations of information
are not consistent with all participants' individual conceptions and across the
decision space, then the results of models and decision support systems will
not contribute to resolving disputes or producing collaborative decisions.
Research is needed to identify typical spatial conceptualizations of problems
for classes of spatial problems and for typical stances in these problems.
Evaluations of the effectiveness of different existing methods of representing
these conceptualizations can provide useful input to the design of spatial
decision support systems and models for collaborative decision making.
Modeling with multiple data sets, multiple models, and multiple problem
In a computing environment designed to support collaborative decision making
between several groups, often there is not complete agreement upon the data set
to be used and the model to be employed. Thus it may be necessary to apply a
model to any of a number of different data sets or to use any data set in all
models. Thus a computing environment must be available to support multiple
models and data sets, plus an interface which can aid in comparing
alternatives, measuring differences between them, and presenting/viewing such
For a spatial CSDM example, consider a situation in which one group in a decision process would like to use a median location model to locate ambulance stations in an urban area, while another group insists on the use of a maximal covering problem. While both groups agree to use the same data set, two different models will be employed. In order to communicate between groups, one model's output (say sites and weighted distance) needs to be compared in terms of the other model's objective (coverage within some distance standard).
Although this sounds simple, negotiation would require generating and
presenting compromise solutions. To do that would require one of two
techniques: 1) a multiobjective model which supports both objectives, or 2) a
methodological bridge which can systematically integrate two independent models
with weights and structural conditions which can be used to identify compromise
solutions. The first approach requires that the integrated model exists in the
first place and that all integration is done in advance and has been
anticipated. The second approach has never been attempted or theoretically
A major need in the support of collaboration in spatial decision making is the
capability to generate alternatives that achieve specific objectives or have
specific spatial qualities. Frequently, however, decision makers are not able
to specify all their objectives completely, thus some objectives remain hidden
or private. Brill, Hopkins and others have argued that when hidden objectives
are exposed, solutions which were once considered inferior can now be
considered noninferior. This argument leads to a natural conclusion: since it
is probably impossible to elicit all objectives from groups of decision makers,
it is important to be able to generate both noninferior solutions and close to
noninferior solutions. Techniques that support collaborative decision making
must be capable of generating close-to-optimal alternatives, of searching for
good compromise solutions, and of searching for solutions that differ spatially
but are not very different in performance.
Collaborative decision making involves generating feasible alternatives among
many individuals or groups. It is often difficult to formulate problems to
include feasibility factors such as political aspects, human perceptions,
safety factors, aesthetics, etc. Some process of visualizing, evaluating, and
adjusting model generated alternatives is required to develop a feasible group
consensus. Techniques need to be developed to intelligently explore the
decision space of spatial problems and to look for good (feasible) solutions to
Revealing preferences and objectives
Economists often infer the relative value of various objectives of a decision
maker by determining which weights yield an optimal choice similar to that made
by the decision maker, or by asking a decision maker to choose between a series
of pairwise comparisons. Understanding which objectives are important, whether
voiced or not, can be important in reaching an accord. Clearly, systems which
can help identify underlying preferences or objectives can aid collaboration
Consider the following example: suppose a decision maker had selected a
specific route for a highway alignment. According to an analysis based on
tradeoff of objectives, it is clear that the decision maker is interested in
ensuring that a specific town is close to the route. Using this information,
it is then possible to generate tradeoffs in the route selection based on total
vehicle miles traveled by others vs. the total vehicle miles traveled by people
in this specific town. The decision maker may then see the cost of meeting his
desired goal (getting close to a specific town) as a function of the cost to
all others. Without identifying what objectives are present or the relative
importance of those objectives, it may be impossible to tease entirely rational
designs or negotiate a best compromise in a collaborative decision making
setting. An important research objective is to look at alternatives for
capturing decisions and revealing preferences in spatial problems, and to test
various approaches in prototypes.
Problems of presenting multiple solutions and visualizing differences
The presentation and comparison of alternative solutions in many spatial
decision support systems is poorly conceived at best. Few examples exist where
the interface design had an emphasis on the presentation of differences
between alternative solutions. Thus, not only is it important to be able to
study a given solution, but also to be able to spatially compare different
solutions in terms of both objective and decision space attributes. Example
designs and prototypes should be developed to test approaches which might be
useful to accomplish this task.
Using animation to examine sensitivity to change and to examine change over
Animation can provide a tool for viewing how a solution changes as a result of
changing model parameters. After a model is solved, it is often important to
understand how sensitive a given solution is to the original model parameters.
Often this is done by systematically changing the model's parameters to see if
changes result in the same solutions--a process which can be very time
consuming and produce results which are difficult to compare. Currently, for
most spatial optimization models, there is no automatic way in which to
generate and view such demonstrations of model sensitivity. Animating
sensitivity analysis can aid in the understanding of input data error and
uncertainty, and may allow complex spatial models and their solutions to be
viewed in a form which may help reveal specific nuances (e.g. why is this area
Given that some model solutions are temporal as well (spanning up to 20 decades), animation may also be an important tool for viewing how a solution changes over time. Insight into temporal change may provide some important common ground for a group of decision makers who are considering a number of different solutions.
To address some of these research themes, the following research is planned at UCSB:
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Comments to Karen Kemp